英语翻译3.1 TrackingMore often than not,the false positive detec

英语翻译
3.1 Tracking
More often than not,the false positive detections from our license plate detector were erratic,and if on the car body,their position was not temporally consistent.We use this fact to our advantage by tracking candidate license plate regions over as many frames as possible.Then,only those regions with a smooth trajectory are deemed valid.The tracking of license plates also yields a sequence of samplings of the license plate,which can be used as input to a super resolution preprocessing step before OCR is performed on them.
Numerous tracking algorithms exist that could be applied to our problem.Perhaps the most well-known and popular is the Kanade-Lucas-Tomasi (KLT) tracker.The KLT tracker makes use of a Harris corner detector to detect good features to track in a region of interest (our license plate) and measures the similarity of every frame to
the first allowing for an affine transformation.Sullivan etal.make use of a still camera for the purposes of tracking vehicles by defining regions of interest (ROI) chosen to span individual lanes.They initiate tracking when a certain edge characteristic is observed in the ROI and make predictions on future positions of vehicles.Those tracks with a majority of accurate predictions are deemed valid.Okumaet al.use the Viola and Jones framework to detect hockey players and then apply a mixture particle filter using the detections as hypotheses to keep track of the players.
Although each of these tracking methods would probably have worked well in our application,we chose a far simpler approach which worked well in practice.Because detecting license plates is efficient we simply run our detector on each frame and for each detected plate we determine whether that detection is a new plate or an instance of a plate already being tracked.To determine whether a detected plate is new or not,the following conditions are checked:
• the plate is within T pixels of an existing tracker
• the plate is within T` pixels of an existing tracker and the plate is within θ degrees of the general direction of motion of the plates in the tracker’s history
If any of these are true,the plate is added to the corresponding tracker,otherwise a new tracker is created for that plate.In our application T` was an order of magnitude larger than T.Our tracking algorithm was also useful for discarding false positives from the license plate detector.The erratic motion of erroneous detections usually resulted in the initiation of several trackers each of which stored few image sequences.Image sequences of 5 frames or fewer were discarded.
3.2 Character Recognition
It was our initial intent to apply a binarization algorithm,such as a modified version of Niblack’s algorithm as used by Chen and Yuille,on the extracted license plate images from our detector,and then use the binarized image as input to a commercial OCR package.We found,however,that even at a resolution of 104 × 31 the OCR packages we experimented with yielded very poor results.Perhaps this
should not come as a surprise considering the many custom OCR solutions used in existing LPR systems.
谢绝直接使用工具而读起来不通的！

3.1跟踪
通常,那些假阳性检测器检测从我们的车牌是无规律,若在汽车车身的时间,他们的情况并不一致.我们用这个事实对我们有利的候选车牌区域跟踪了尽可能多帧.然后,只有那些地区与一个光滑轨迹被认为是有效的.跟踪牌照也产生一个序列的车牌目的,可作为输入一个超级分辨率OCR前进行预处理环节.
存在众多的跟踪算法可以应用到我们的问题.也许最著名和最受欢迎的是Kanade-Lucas-Tomasi(KLT)盈.利用KLT跟踪器的哈里斯角落的检测特点,探测器进行良好的感兴趣区域(我们)和措施车牌每帧的相似性
第一个让仿射变换.沙利文etal.利用还相机为目的,通过定义区域跟踪车辆(ROI)选择感兴趣的跨越各自的跑道.他们倡导当一个边缘特征跟踪观察到投资回报率和预测未来的位置的车辆.那些轨道与大多数准确的预言被认为是有效的.Okumaet王汝成等使用百合和琼斯检测曲棍球运动员框架,然后应用混合使用检测作为粒子滤波跟踪这个假说的球员.
尽管每一种跟踪方法可能会工作得很好我们的申请,我们选择在一个更简单的方法在实践中效果良好.因为是有效的检测车牌简单运行我们的探测器在每一帧和每个侦测我们确定,检测板是一种新型的钢板或实例的板已被追踪.来确定一个检测是新的,板检查下列条件:
在T•盘子的像素的一个现有的盈
在T•平板的像素的一个现有的跟踪和板材在θ度的一般运动的方向该板在历史上的地位
如果这些都是真实的,盘子被添加到相应的跟踪,否则一个算法是为了那盘.在应用软件T”是一个数量级比我们追踪算法T用于丢弃的误报率从车牌侦测器.不稳定的运动检测通常导致错误开始的几个追踪每几个序列图像的存储.5帧图像序列或减少discarded.问题补充:
3.2字符识别
这是我们的初衷,运用一个二值化算法,如修改的版本的算法Niblack使用,陈和Yuille提取车牌图像从我们的探测器,然后使用图像二值作为输入到一个商业OCR技术包裹.我们发现,然而,即使在104年的一项决议,×31 OCR包裹我们尝试取得了很差的结果.也许这
不应该感到惊讶考虑许多定制解决方案应用于现有LPR光学系统.